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Prosodic elements are considered the main parts of the Prosody science. So, learning these elements and the system that governs them plays a critical role in traditional prosody. In addition, recognizing the different types of elements (Salim va Mozahaf) shapes a big part of prosody. By realizing the defects of the traditional system, prosodists created new prosodic systems. In this way, each of them introduced their desired prosodic elements. But in the meantime, the explanation of the relationship between the elements in the new prosody and the suggestion of a structure for them was neglected. As a result, it confused the basics of prosody and caused difficulty in learning it. This research aims to identify the relationships between the elements and structurally explain them. The results of the study show two types of structural relationships between Persian prosodic elements. The first is formed based on the increase and decrease of the last syllables of the elements, and the second is based on the old concept of prosodic circles. By using it, all elements can be derived from the circles we call ‘elemental circles’. Based on these two types of relationships, two new prosodic structures are introduced: linear structure and circular structure. The common result shows that, regardless of rare ideas, the number of correct elements of Persian Prosody reaches 44, which are: 1 one-syllable, 2 two-syllable, 7 three-syllable, 13 four-syllable, and 21 five-syllable elements.

Lyrics generation presents unique challenges, particularly in achieving precise syllable control while adhering to song form structures such as verses and choruses. Conventional line-by-line approaches often lead to unnatural phrasing, underscoring the need for more granular syllable management. We propose a framework for lyrics generation that enables multi-level syllable control at the word, phrase, line, and paragraph levels, aware of song form. Our approach generates complete lyrics conditioned on input text and song form, ensuring alignment with specified syllable constraints. Generated lyrics samples are available at: https://tinyurl.com/lyrics9999

In this review article authors are trying to put light on text to speech synthesis of Assamese language using unit selection concatenative speech synthesis technique. Assamese is one of the North East Indian languages spoken by millions of people. This article tries to highlight some major difficulties when developing the synthesizer. The speech units used to concatenate for building the synthesizer is syllable. Assamese is a syllable centric language and syllable based concatenation gives more natural sound. A discussion is done with phoneme and diphone units as well. There is also a short overview on development process of the Assamese synthesizer on festival framework as a part of review. Another challenging task the researchers dealt with is building speech corpus for a low resource language like Assamese.

We find a family of groups generated by a pair of parabolic elements in which every relator must admit a long subword of a specific form. In particular, this collection contains groups in which the number of syllables of any relator is arbitrarily large. This suggests that the existing methods for finding non-free groups with rational parabolic generators may be inadequate in this case, as they depend on the presence of relators with few syllables. Our results rely on two variants of the ping-pong lemma that we develop, applicable to groups that are possibly non-free. These variants aim to isolate the group elements responsible for the failure of the classical ping-pong lemma.

The Unit Selection based Synthesis (USS) inventory contains a large number of fundamental units with differing prosodic and spectral features. Based on the target and concatenation costs, a USS system chooses relevant units from its inventory. We use the Festival framework to develop a USS system that is based on syllables. In any USS system, it’s impossible to include all potential syllables in a local language from all possible contexts

Hayes & Kaun (1996) argue that the mapping of syllables onto a metrical grid in textsetting is sensitive to natural duration, not just categorical weight (heavy or light). Most of their evidence, however, derives the final lengthening effects, which admit of another possible analysis (Halle 2004). Drawing on a corpus of 2,371 popular songs in English, I confirm that even when one controls for final lengthening and other factors, the setting of syllables to a discrete grid is sensitive to natural duration. Moreover, onset effects reveal that the domain of weight for textsetting is not the syllable, rime, or vowel-to-vowel interval, but rather the interval between p-centers (perceptual centers). Finally, I argue that the textsetting grammar invokes both natural duration and categorical weight; weight mapping cannot be reduced to one or the other.

Understanding how genes, drugs and neural circuits influence behavior requires the ability to effectively organize information about similarities and differences within complex behavioral datasets. Motion Sequencing (MoSeq) is an ethologically inspired behavioral analysis method that identifies modular components of three-dimensional mouse body language called ‘syllables’. Here, we show that MoSeq effectively parses behavioral differences and captures similarities elicited by a panel of neuroactive and psychoactive drugs administered to a cohort of nearly 700 mice. MoSeq identifies syllables that are characteristic of individual drugs, a finding we leverage to reveal specific on- and off-target effects of both established and candidate therapeutics in a mouse model of autism spectrum disorder. These results demonstrate that MoSeq can meaningfully organize large-scale behavioral data, illustrate the power of a fundamentally modular description of behavior and suggest that behavioral syllables represent a new class of druggable target.By analyzing hundreds of mice treated with a library of neuro- and psychoactive drugs, Wiltschko et al. show that Motion Sequencing can effectively discriminate and categorize drug effects and link molecular targets to behavioral syllables.

In this article, we describe a method for automatically detecting syllable nuclei in order to measure speech rate without the need for a transcription. A script written in the software program Praat (Boersma & Weenink, 2007) detects syllables in running speech. Peaks in intensity (dB) that are preceded and followed by dips in intensity are considered to be potential syllable nuclei. The script subsequently discards peaks that are not voiced. Testing the resulting syllable counts of this script on two corpora of spoken Dutch, we obtained high correlations between speech rate calculated from human syllable counts and speech rate calculated from automatically determined syllable counts. We conclude that a syllable count measured in this automatic fashion suffices to reliably assess and compare speech rates between participants and tasks.

Defining and measuring readability in the context of financial disclosures becomes important with the increasing use of textual analysis and the Securities and Exchange Commission's plain English initiative. We propose defining readability as the effective communication of valuation-relevant information. The Fog Index—the most commonly applied readability measure—is shown to be poorly specified in financial applications. Of Fog's two components, one is misspecified and the other is difficult to measure. We report that 10-K document file size provides a simple readability proxy that outperforms the Fog Index, does not require document parsing, facilitates replication, and is correlated with alternative readability constructs.